Considering that many industrial and military noise environments are non-Gaussian, and that energy metrics (i.e., a weighted equivalent energy, Leq) commonly used to assess the adverse effects of a noise exposure on hearing are suitable metrics only for Gaussian noise, the investigator proposes to develop and test the validity of an alternate approach to noise analysis for the purpose of hearing conservation which will more precisely predict the audiometric and morphological consequences of an exposure. Specifically, the investigator will show that an energy metric in combination with the statistical metrics of frequency- and time-domain kurtosis and the joint peak-interval histogram will provide necessary (and possibly sufficient) information on essentially any industrial noise environment to evaluate its potential for causing hearing loss. Animals (chinchillas) will be exposed to non-Gaussian, non-stationary noises having the same energy and spectra of a Gaussian reference noise (two reference noise conditions will be used having spectral and level parameters typical of an industrial environment). Noise stimuli designed with very specific but diverse statistical properties will be produced using recently-designed software. New analytical methods developed in the investigator's laboratories over the past three years involving the wavelet transform and higher-order cumulant-based inverse filtering will be applied to the continuously sampled noise stimuli to extract temporal and peak statistical properties of the noise stimulus. Effects on hearing, quantified by pure-tone thresholds, otoacoustic emissions, and sensory cell losses will be correlated with the noise metrics to establish the validity of these metrics. Considering that the algorithms for the analytical methods mentioned above have been developed and can be integrated into commercial noise analysis systems, the successful outcome of these experiments can lay the foundations for a new and more generalized approach, as well as a more accurate approach to the evaluation of noise environments. The suggested analytical methods may also have some engineering applications in identifying features of the noise environment that can be reduced or altered at their source. It is important to understand what features of a noise are most hazardous to hearing in order that engineering procedures can be implemented on specific noise-producing components of machinery or designed into hearing protective devices. A database consisting of results from at least 408 subjects run through one of two control or 32 different complex noise exposure conditions will be constructed. A large sample size and a wide variation of exposure parameters are necessary to insure statistical power for the correlations that will be developed. While the experimental methods in this proposal are routine, the demonstration of good correlations between the proposed metrics and hearing loss following realistic and diverse exposure conditions has widespread implications for industrial safety standards and noise measurement systems.